Economy of Free
|If you are a producer using free payments, you are warmly welcome to contribute with factual data here.|
This is an attempt to formalize and quantify a free culture economy based on free payment.
- 1 Introduction
- 2 Revenue curve
- 2.1 Audience
- 2.2 Number of free payments
- 2.3 Power law zone
- 2.4 Freeloaders zone
- 2.5 Free Payment Conversion Factor (FPCF)
- 2.6 Factors influencing revenue curves
- 2.7 Revenue curve characterization
- 3 Footnotes
There is no doubt that an economy based on free payments will have very different characteristics than a traditional commodity economy. Among other things, most of the established methods for predicting prices and revenus, like supply and demand curves, won't apply.
In order to develop realistic business cases and to devise appropriate maximization strategies, we need to gain a much better understanding of the fundamental characteristics of the economy of free.
There is very little hard data available to characterize the revenue curve of producers using free payments. Nonetheless, there is a high probability that the basic distribution properties of the curve will be similar to other form of online contribution for which we have more knowledge. According to many sources, online contribution exhibit a power law distribution (or Pareto distribution) and it makes very much sense that free payments revenue will exhibit similar characteristics.
On the following theoretical revenue curve of a given free product, the X axis represent the distribution of customers contributing free payments and the Y axis is the free payment value.
|F||Number of free payments|
|Zone 1||Power law zone|
|Zone 2||Freeloaders zone|
Point A on the graph represents the audience size or the number of customers for the product. How it is actually measured depends on the type of products. Example:
- For downloadable products: number of downloads
- For streaming: number of (unique?) users
- For web publishing: number of unique visitors
- For video: number of unique viewers
Number of free payments
Point F correspond to the number of free paying customers, whatever the value of the payment is.
Power law zone
This is the zone where the power law distribution of free payments occurs and where, as for many other types of online contributions, few people provide most of the contributions. In reality, this curve is likely to display multiple "bumps and bruises" depending of factors like suggested prices, perceived prices and payments programs.
This zone corresponds to the vast majority of customers who do not pay for the product.
Free Payment Conversion Factor (FPCF)
Let's define FPCF as the relation between the quantity (and not the value) of free payments and the total number of consumers.
FPCF = F ÷ A = %
This number will sorely vary from one producer to the other. Nonetheless, it can be expected that its mean value for a more or less specific market will depend greatly of the overall awareness and sensibilization of consumers to the free payment business model.
Knowing the average FPCF for a given market allows to do basic predictions on producers revenue.
Number of free payments = Size of audience X FPCF
Factors influencing revenue curves
Other than factors related to the product itself (quality, liking, etc.), what could influence the revenue curve of a producer using free payments?
It is very likely that the overall economy health, at the regional and global level, will greatly influence the revenue curve, and probably more than in a traditional economy. Much more people will choose not to pay if they perceive they can't afford it.
Awareness and sensibilization
The average FPCF for a given market will greatly depend on general awareness and sensibilization of the digital population regarding the free payment model and its advantages.
Providing information like suggested prices and product cost structures will likely influence revenue curve characteristics. More generally, any information has the potential to influence some consumers into paying a different amount, positively if the information is perceive as such and negatively otherwise. Even things like the perceived price of a product, which is likely to have an important effect on the curve, is the result of numerous information customers gather around themselves.
But, information itself is not enough. Information needs to be properly communicated. Positive information won't help if it remains unknown. Information flow is the real contributing factor here, the information flow between the producer and its consumers. To maximize the revenue curve, you need to maximize the flow of positive information.
As a result, an economy of free will most likely favor businesses able to maximize information flow to its customers. It also means it will favor businesses that have nothing to hide.
Sense of ownership
The sense of ownership and concern of a customer for a product or for the overall producer's work is likely to influence the amount he is ready to pay. Nurturing a community of true fans is one way to develop the sense of ownership among its customers. But, at the end, I believe the sense of ownership is a derivative product of information flow.
Payments programs and facilities
Things like monthly payment facilities and online tip jar may certainly have an influence on the revenue curve.
Rewards and recognitions programs
Offering non monetary incentives (like status based rewards) or recognition programs (maintaining a list of donors for example) may also have a positive influence on the revenue curve.
According to some, the novelty factor among consumers regarding this model may boost revenus of early adopters.
Revenue curve characterization
Right now, there is not enough data to describe the properties of the revenue curve.
Data for characterization are collected on the page Data Sets. Collected data should be analyzed by taking into account any factor (see above) that could influence the revenue curve.